Robots are electro-mechanical devices which can be used to manipulate objects via a series of links. The links are interconnected by articulations or actuator-driven robotic joints. Each joint in a typical robot represents an independent control variable or degree of freedom (DOF). End-effectors are the particular links used to perform a given work task, such as grasping a work tool or otherwise acting on an object. Precise motion control of a robot through its various DOF may be organized by task level: object level control, i.e., the ability to control the behavior of an object held in a single or cooperative grasp of the robot, end-effector control, and joint-level control. Collectively, the various control levels cooperate to achieve the required robotic dexterity and work task-related functionality.
The structural complexity of a dexterous robot is largely dependent upon the nature of the work task. During object manipulation, it is necessary to track the manipulator with respect to its environment, i.e., the system state. Without such tracking, the robot remains ignorant of the outcome of its actions during a given work sequence. However, for dexterous robots having a relatively high number of DOF, the monitoring and tracking of the system state is a highly complicated endeavor. Hundreds of individual sensor signals are commonly encountered, with difficulty arising in the processing and determination of the relevance of the various sensor signals to the ultimate determination of the present system state. Thus, existing robot control systems and control methodologies may be less than optimal when used for state tracking and monitoring of a relatively high DOF dexterous robot.